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Relevant bibliographies by topics / Traffic flow

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Academic literature on the topic 'Traffic flow'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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Journal articles on the topic "Traffic flow"

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J, Cynthia, G. Sakthi Priya, C. Kevin Samuel, Suguna M, Senthil J, and S. Abraham Jebaraj. "Traffic Flow Forecasting Using Machine Learning Techniques." Webology 18, no. 04 (September 28, 2021): 1512–26. http://dx.doi.org/10.14704/web/v18si04/web18295.

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Congestion due to traffic, results in wasted fuel, increase in pollution level, increase in travel time and vehicular queuing. Smart city initiatives are aimed to improve the quality of urban life. Intelligent Transportation System (ITS) provides solution for many smart city projects, as they capture real time data without any fixed infrastructure. The real-time prediction of traffic flow aids in alleviating congestion. Accurate and timely prediction on the future traffic flow helps individual travellers, public transport, and transport planning. Existing systems are designed to predict specific traffic parameters like weekday, weekend, and holidays. This research presents a machine learning based traffic flow forecasting for the city of Bloomington, US not with any precise parameter. The day-wise dataset for the 5 areas is taken from Jan 1, 2017 to Dec 31, 2019. The algorithm used for implementation is Support Vector Regression (SVR) and Long Short-Term Memory (LSTM). LSTM algorithm provides better traffic prediction with least root means square error value.

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Singh, Rahul, Kamini Rawat, and Aarti Kadiyan. "Simulation of Traffic Flow in Presence of Traffic Light using Cellular Automata." Indian Journal of Applied Research 4, no. 6 (October 1, 2011): 191–93. http://dx.doi.org/10.15373/2249555x/june2014/60.

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Milius, Susan. "Ant Traffic Flow." Science News 162, no. 25/26 (December 21, 2002): 388. http://dx.doi.org/10.2307/4013963.

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Schmidt, Werner, Stephan Borgert, Albert Fleischmann, Lutz Heuser, Christian Müller, and Immanuel Schweizer. "Smart Traffic Flow." HMD Praxis der Wirtschaftsinformatik 52, no. 4 (May 19, 2015): 585–96. http://dx.doi.org/10.1365/s40702-015-0146-0.

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Seton-Rogers, Sarah. "Altered traffic flow." Nature Reviews Cancer 15, no. 10 (September 24, 2015): 574. http://dx.doi.org/10.1038/nrc4021.

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Jones, W. D. "Forecasting traffic flow." IEEE Spectrum 38, no. 1 (January 2001): 90–91. http://dx.doi.org/10.1109/6.901153.

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Pogrebnyak, M. A. "Traffic Flow Model." Mathematical Models and Computer Simulations 15, no. 3 (May 17, 2023): 436–44. http://dx.doi.org/10.1134/s2070048223030146.

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Pešić, Dalibor, Boris Antić, Emir Smailovic, and Bojana Todosijević. "The impact of the average traffic flow speed on occurrence risk of road crash." Put i saobraćaj 65, no. 2 (July 9, 2019): 29–36. http://dx.doi.org/10.31075/pis.65.02.05.

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Traffic flow characteristics have a significant impact on occurrence risk of road crash. The most important characteristics of the traffic flow, the impact of which is the subject of numerous studies, are the traffc flow, density, average traffic flow speed, dispersion of traffic flow speeds, as well as the contents of vehicle in traffic flow. These characteristics are in strong correlation between each other, so changes in one parameter conditional make change of other parameters. Research shows that speed-related traffic flow parameters have a significant impact on occurrence risk of road crash. Therefore, in this study an analysis of the impact of the change in the average speed of the traffic flow on the risk of an accident occurred. The research includes a section of the single carriageway from Preljina to Ljig. After the construction of the highway in the stated section of the signle carriageway, a change in the characteristics of the traffic flow occurred, with this study examining the impact of changing the average speed of the traffic flow to the occurrence risk of road crash. The connection between the speed of traffic flow and the risk of accidents has been confirmed in this study, so with the increase in average speed the risk of accidents increases.

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Hrytsun, Oleh. "Impact of traffic volume and composition on the change in the speed of traffic flow." Transport technologies 2023, no. 1 (June 6, 2023): 12–20. http://dx.doi.org/10.23939/tt2023.01.012.

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The problem of the change in the speed of traffic flow at different traffic volumes and compositions is researched in this study. The section of the road network with different geometric parameters (descent, ascent and horizontal section) was chosen for the study. The method of investigation of traffic flow`s speed and factors which have an impact on the reduction of road network capacity are analyzed. The change in the coefficients of the unevenness of traffic flow by hours of the day in the studied area was determined and a graph of the distribution of traffic volume by hours of the day was built. A diagram of the section was built to determine the speed of the traffic flow, on which the movement along the horizontal section, uphill and downhill movement is present. It was established that at a traffic volume of 700-800 p.c.u./h, the traffic flow moves at a constant speed (up to 10-15 km/h). Cumulative curves of traffic flow speed` distribution characterizing modes of traffic flow on the road network were built. It is determined that at volume-capacity ratio 0< z ≤ 0,4 on three investigated sections traffic flow moves with the speed from 35 km/h to 59 km/h. In the specialized software product PTV VISSIM, the simulation of the traffic flow on the horizontal section, ascent and descent has been developed. Using the MATHLAB software environment, it is shown how the speed of the traffic flow changes depending on the volume-capacity ratio and the share of the heterogeneous traffic flow. It was established that the highest speed of the flow is observed during the downhill movement – 58.62 km/h at the volume-capacity ratio – 0.13 and the share of heterogeneous traffic flow – 1.0 (100% cars). At a volume-capacity ratio of 0.88 and existing road conditions, the speed of traffic flow on the horizontal section and during uphill movement is almost the same (the average deviation is 6%). It can be explained by the fact that at a volume-capacity ratio of 0.88, traffic flow is in the traffic jam, hence, the speed of movement on the three sections is the same.

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Ganesh, V., M. Mohansairam, P. Manoj Kumar, K. Laxmi Bharadwaj, and Md Asif Baba. "A Study on Saturated Traffic Flow at Signalized Intersections Under Mixed Traffic Conditions." International Journal of Research Publication and Reviews 4, no. 3 (March 17, 2023): 2939–46. http://dx.doi.org/10.55248/gengpi.2023.4.33649.

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Dissertations / Theses on the topic "Traffic flow"

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Cappiello, Alessandra 1972. "Modeling traffic flow emissions." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/84328.

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Godvik, Marte. "On a Traffic Flow Model." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-2296.

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Paper 1 Electronic version of an article published asJournal of Hyperbolic Differential Equations (JHDE) Year: 2008 Vol: 5 Issue: 1 (March 2008) Page: 45 - 63, DOI:10.1142/S0219891608001428 © [copyright World Scientific Publishing Company] http://ejournals.ebsco.com/Direct.asp?AccessToken=8PUU3U4V0W9F3OY0909K09P3FOXYVWNOW&Show=Object&msid=943592237

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Gebresilassie, Mesele Atsbeha. "Spatio-temporal Traffic Flow Prediction." Thesis, KTH, Geoinformatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212323.

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The advancement in computational intelligence and computational power and the explosionof traffic data continues to drive the development and use of Intelligent TransportSystem and smart mobility applications. As one of the fundamental components of IntelligentTransport Systems, traffic flow prediction research has been advancing from theclassical statistical and time-series based techniques to data–driven methods mainly employingdata mining and machine learning algorithms. However, significant number oftraffic flow prediction studies have overlooked the impact of road network topology ontraffic flow. Thus, the main objective of this research is to show that traffic flow predictionproblems are not only affected by temporal trends of flow history, but also by roadnetwork topology by developing prediction methods in the spatio-temporal.In this study, time–series operators and data mining techniques are used by definingfive partially overlapping relative temporal offsets to capture temporal trends in sequencesof non-overlapping history windows defined on stream of historical record of traffic flowdata. To develop prediction models, two sets of modeling approaches based on LinearRegression and Support Vector Machine for Regression are proposed. In the modelingprocess, an orthogonal linear transformation of input data using Principal ComponentAnalysis is employed to avoid any potential problem of multicollinearity and dimensionalitycurse. Moreover, to incorporate the impact of road network topology in thetraffic flow of individual road segments, shortest path network–distance based distancedecay function is used to compute weights of neighboring road segment based on theprinciple of First Law of Geography. Accordingly, (a) Linear Regression on IndividualSensors (LR-IS), (b) Joint Linear Regression on Set of Sensors (JLR), (c) Joint LinearRegression on Set of Sensors with PCA (JLR-PCA) and (d) Spatially Weighted Regressionon Set of Sensors (SWR) models are proposed. To achieve robust non-linear learning,Support Vector Machine for Regression (SVMR) based models are also proposed.Thus, (a) SVMR for Individual Sensors (SVMR-IS), (b) Joint SVMR for Set of Sensors(JSVMR), (c) Joint SVMR for Set of Sensors with PCA (JSVMR-PCA) and (d) SpatiallyWeighted SVMR (SWSVMR) models are proposed. All the models are evaluatedusing the data sets from 2010 IEEE ICDM international contest acquired from TrafficSimulation Framework (TSF) developed based on the NagelSchreckenberg model.Taking the competition’s best solutions as a benchmark, even though different setsof validation data might have been used, based on k–fold cross validation method, withthe exception of SVMR-IS, all the proposed models in this study provide higher predictionaccuracy in terms of RMSE. The models that incorporated all neighboring sensorsdata into the learning process indicate the existence of potential interdependence amonginterconnected roads segments. The spatially weighted model in SVMR (SWSVMR) revealedthat road network topology has clear impact on traffic flow shown by the varyingand improved prediction accuracy of road segments that have more neighbors in a closeproximity. However, the linear regression based models have shown slightly low coefficientof determination indicating to the use of non-linear learning methods. The resultsof this study also imply that the approaches adopted for feature construction in this studyare effective, and the spatial weighting scheme designed is realistic. Hence, road networktopology is an intrinsic characteristic of traffic flow so that prediction models should takeit into consideration.

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Golden, Gaylynn. "Effects of driver characteristics and traffic composition on traffic flow." Master's thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-10242009-020010/.

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Kim, Youngho. "Online traffic flow model applying dynamic flow density relations." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=964751909.

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Reed, Brandon B. "Continuum Traffic Flow at a Highway Interchange." University of Akron / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1196711036.

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Wall, Zach R. "Traffic management and control utilizing a microscopic model of traffic dynamics /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5922.

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Yan, Li. "On the traffic flow control system." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B39431174.

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Yan, Li, and 顏理. "On the traffic flow control system." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39431174.

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Heller, Mark D. "Behavioral analysis of network flow traffic." Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/5108.

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Approved for public release, distribution unlimited
Network Behavior Analysis (NBA) is a technique to enhance network security by passively monitoring aggregate traffic patterns and noting unusual action or departures from normal operations. The analysis is typically performed offline, due to the huge volume of input data, in contrast to conventional intrusion prevention solutions based on deep packet inspection, signature detection, and real-time blocking. After establishing a benchmark for normal traffic, an NBA program monitors network activity and flags unknown, new, or unusual patterns that might indicate the presence of a potential threat. NBA also monitors and records trends in bandwidth and protocol use. Computer users in the Department of Defense (DoD) operational networks may use Hypertext Transport Protocol (HTTP) to stream video from multimedia sites like youtube.com, myspace.com, mtv.com, and blackplanet.com. Such streaming may hog bandwidth, a grave concern, given that increasing amounts of operational data are exchanged over the Global Information Grid, and introduce malicious viruses inadvertently. This thesis develops an NBA solution to identify and estimate the bandwidth usage of HTTP streaming video traffic entirely from flow records such as Cisco's NetFlow data.

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Books on the topic "Traffic flow"

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Treiber, Martin, and Arne Kesting. Traffic Flow Dynamics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32460-4.

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Kessels, Femke. Traffic Flow Modelling. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-78695-7.

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As, S. C. van. Traffic flow theory. 3rd ed. [Pretoria]: SARB Chair in Transportation Engineering, Dept. of Civil Engineering, University of Pretoria, 1990.

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United States. Federal Highway Administration. Office of Highway Information Management. Traffic monitoring guide. [Washington, D.C.]: US Dept. of Transportation, Federal Highway Administration, Office of Highway Information Management, 1992.

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Hébuterne, Gérard. Traffic flow in switching systems. Boston: Artech House, 1987.

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Knoop, Victor L., and Winnie Daamen, eds. Traffic and Granular Flow '15. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33482-0.

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Hoogendoorn, Serge P., Stefan Luding, Piet H. L. Bovy, Michael Schreckenberg, and Dietrich E. Wolf, eds. Traffic and Granular Flow ’03. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-28091-x.

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Fukui, Minoru, Yuki Sugiyama, Michael Schreckenberg, and Dietrich E. Wolf, eds. Traffic and Granular Flow’01. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-10583-2.

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Zuriguel, Iker, Angel Garcimartin, and Raul Cruz, eds. Traffic and Granular Flow 2019. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55973-1.

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Kozlov, Valery V., Alexander P. Buslaev, Alexander S. Bugaev, Marina V. Yashina, Andreas Schadschneider, and Michael Schreckenberg, eds. Traffic and Granular Flow '11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39669-4.

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Book chapters on the topic "Traffic flow"

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Holmes, Mark H. "Traffic Flow." In Texts in Applied Mathematics, 233–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24261-9_5.

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Holmes, Mark H. "Traffic Flow." In Texts in Applied Mathematics, 205–64. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-87765-5_5.

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Salter, R. J. "Flow, Speed and Density Relationships for Highway Flow." In Traffic Engineering, 29–33. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-10800-8_8.

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Treiber, Martin, and Arne Kesting. "Traffic Flow Breakdown and Traffic-State Recognition." In Traffic Flow Dynamics, 355–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32460-4_18.

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Spellman, Frank R. "Electron Flow = Traffic Flow." In The Science of Electric Vehicles, 3–6. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003332992-2.

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Gene Hawkins Jr., H. "Traffic Flow Characteristics for Uninterrupted-Flow Facilities." In Traffic Engineering Handbook, 203–34. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781119174738.ch7.

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Kessels, Femke. "Introduction to Traffic Flow Modelling." In Traffic Flow Modelling, 1–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78695-7_1.

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Kessels, Femke. "The Fundamental Diagram." In Traffic Flow Modelling, 21–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78695-7_2.

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Kessels, Femke. "Microscopic Models." In Traffic Flow Modelling, 35–51. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78695-7_3.

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Kessels, Femke. "Macroscopic Models." In Traffic Flow Modelling, 53–81. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78695-7_4.

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Conference papers on the topic "Traffic flow"

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Wolf, D. E., M. Schreckenberg, and A. Bachem. "Traffic and Granular Flow." In International Workshop on Traffic and Granular Flow. WORLD SCIENTIFIC, 1996. http://dx.doi.org/10.1142/9789814531276.

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Nagai, Ryoichi. "Complex Dynamic States in Multi-phase Traffic Model." In FLOW DYNAMICS: The Second International Conference on Flow Dynamics. AIP, 2006. http://dx.doi.org/10.1063/1.2204519.

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Li, Perry Y., Roberto Horowitz, Luis Alvarez, Jonathan Frankel, and Anne M. Robertson. "Traffic Flow Stabilization." In Future Transportation Technology Conference & Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1995. http://dx.doi.org/10.4271/951896.

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Hong Liu and Wang Hui. "Hybrid traffic flow model." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4594308.

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Montigny-leboeuf, Annie, and Tim Symchych. "Network Traffic Flow Analysis." In 2006 Canadian Conference on Electrical and Computer Engineering. IEEE, 2006. http://dx.doi.org/10.1109/ccece.2006.277589.

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Horn, Berthold K. P. "Suppressing traffic flow instabilities." In 2013 16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/itsc.2013.6728204.

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Po, Laura, Federica Rollo, Jose Ramon Rios Viqueira, Raquel Trillo Lado, Alessandro Bigi, Javier Cacheiro Lopez, Michela Paolucci, and Paolo Nesi. "TRAFAIR: Understanding Traffic Flow to Improve Air Quality." In 2019 IEEE International Smart Cities Conference (ISC2). IEEE, 2019. http://dx.doi.org/10.1109/isc246665.2019.9071661.

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Zhang, Jian-hua, Tao Jiang, Sheng-an Wang, and Jia-wei Ma. "Research of Cellular Automata Traffic Flow Model for Variable Traffic Flow Density." In International Conference on Chemical,Material and Food Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/cmfe-15.2015.172.

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Zhang, Jing. "Traffic Flow Model Based on Power Flow." In 2015 2nd International Conference on Electrical, Computer Engineering and Electronics. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/icecee-15.2015.280.

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Kim, Seongho, Wonho Suh, and Jungin Kim. "Traffic Simulation Software: Traffic Flow Characteristics in CORSIM." In 2014 International Conference on Information Science and Applications (ICISA). IEEE, 2014. http://dx.doi.org/10.1109/icisa.2014.6847475.

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Reports on the topic "Traffic flow"

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Brownlee, N., C. Mills, and G. Ruth. Traffic Flow Measurement: Architecture. RFC Editor, January 1997. http://dx.doi.org/10.17487/rfc2063.

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Brownlee, N., C. Mills, and G. Ruth. Traffic Flow Measurement: Architecture. RFC Editor, October 1999. http://dx.doi.org/10.17487/rfc2722.

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Carlson, Jake. Traffic Flow - Purdue University. Purdue University Libraries, October 2009. http://dx.doi.org/10.5703/1288284315016.

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Brownlee, N. Traffic Flow Measurement: Meter MIB. RFC Editor, January 1997. http://dx.doi.org/10.17487/rfc2064.

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Brownlee, N. Traffic Flow Measurement: Meter MIB. RFC Editor, October 1999. http://dx.doi.org/10.17487/rfc2720.

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Tsirtsis, G., G. Giarreta, H. Soliman, and N. Montavont. Traffic Selectors for Flow Bindings. RFC Editor, January 2011. http://dx.doi.org/10.17487/rfc6088.

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Brownlee, N. Traffic Flow Measurement: Experiences with NeTraMet. RFC Editor, March 1997. http://dx.doi.org/10.17487/rfc2123.

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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, March 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration.

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Handelman, S., S. Stibler, N. Brownlee, and G. Ruth. RTFM: New Attributes for Traffic Flow Measurement. RFC Editor, October 1999. http://dx.doi.org/10.17487/rfc2724.

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Allgood, G. O., R. K. Ferrell, S. W. Kercel, R. A. Abston, C. L. Carnal, and P. I. Moynihan. Traffic flow wide-area surveillance system definition. Office of Scientific and Technical Information (OSTI), November 1994. http://dx.doi.org/10.2172/28302.

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